Definition
Orchestration of ML refers to the automated coordination and management of complex machine learning workflows. It involves defining, scheduling, and monitoring the various stages of an ML lifecycle, from data ingestion and preprocessing to model training, evaluation, and deployment. This process ensures that ML pipelines run efficiently and reliably, integrating seamlessly with existing IT infrastructure. Orchestration is crucial for operationalizing ML models at scale, enabling faster iteration and reducing manual intervention. For instance, an ML orchestration platform can automatically retrain a fraud detection model when new data becomes available and deploy the updated model to production. This differs from simple scripting by providing robust error handling, dependency management, and centralized control. Concepts like this appear in advanced IT certifications and Accumentum training programs focused on cloud and data science.